Intelligent process control and chemical management in reverse osmosis-based wastewater treatment

Efficient wastewater treatment using reverse osmosis (RO) is often constrained by membrane fouling, energy penalties and limited predictive control strategies. Existing studies primarily address fouling analysis or optimisation in isolation, leaving a gap in integrated real-time monitoring and decision support. The study was validated on a pilot-scale RO treatment setup operated under real wastewater conditions. Model performance was assessed over an extended 30-day validation period to capture both slow and accelerated fouling phases. This scale and duration ensure that the forecasting results reflect realistic operational variability and practical applicability. This work introduces a predictive optimisation framework that combines multivariate sensor data (BOD, COD, SDI, TDS, flux, pressure and specific energy consumption) with fouling forecasting models to guide operational adjustments. Experimental validation demonstrated that colloidal fouling caused a ∼50% reduction in permeate flux within 24 h, while specific energy consumption increased by ∼32%. By applying the proposed predictive control, the system reduced energy usage by up to 18% and improved water recovery by 12% compared with fixed-schedule operation. The novelty lies in linking online monitoring, fouling prediction and RO operation optimisation. These results provide a pathway for extending membrane life, reducing operational costs and enhancing the sustainability of wastewater reuse.